提出了一种近似旋律匹配(approximate melody matching)的新方法——线性对齐匹配法,并在此基础上实现了一个哼唱检索(query by humming)系统原型.与已有的基于内容的音乐检索(content-based music retrieval)不同,该算法并非基于近似符号串匹配、统计模型或者特征空间,而是根据相近旋律的音高轮廓在几何上的相似性,将音高和节奏特征一并考虑所设计而成的全新算法.通过实验检验该算法的有效性,在含有3864首乐曲的搜索空间中,检索62段人声哼唱,线性对齐匹配法取得了90.3％的前3位命中率,相比传统的近似符号匹配算法高出11％以上.这一实验结果有力地表明了线性对齐匹配法的有效性,及其应用于大型数字音乐检索引擎的可行性.
may incur significant bandwidth for executing more com- plicated search queries such as multiple-attribute queries. In order to reduce query overhead, KSS (keyword-set search) by Gnawali partitions the index by a set of keywords. However, a KSS index is considerably larger than a standard inverted index, since there are more word sets than there are individual words. And the insert overhead and storage overhead are obviously un- acceptable for full-text search on a collection of documents even if KSS uses the distance window technology. In this paper, we extract the relationship information between query keywords from websites’ queries logs to improve performance of KSS system. Experiments results clearly demonstrated that the improved keyword-set search system based on keywords relationship (KRBKSS) is more efficient than KSS index in insert overhead and storage overhead, and a standard inverted index in terms of communication costs for query.
Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed, these only focus on accuracy which is based on the leave-one-out cross validation procedure. Information-retrieval-related performance measures are always neglected in a kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the leave-one-out procedure. Experiments have proven that our proposed estimators are both effective and stable.
